KIDNEYS

A Study To Assess Effectiveness Of Structured Teaching Program On Knowledge And Attitude Regarding Female Foeticide Among The Primigravida Mothers In a Selected Hospital Of Dehradun Uttarakhand

2026-08-20T11:27:06+00:00

Female foeticide remains a significant social and public health issue in India despite legal measures to prevent sex- selective practices. Inadequate knowledge and unfavorable attitudes toward gender equality contribute to the persistence of this problem. The purpose of the study is to give educational interventions such as Structured Teaching Program (STP) to improve awareness and promote positive attitudes toward the value of the girl child.
Objectives are to assess the knowledge regarding female foeticide among Primigravida mothers, assess the effectiveness of STP on knowledge and attitude regarding female foeticide among Primigravida mothers in a selected hospital.
In this study one group pre- test and post-test design was used to evaluate the effectiveness of structured teaching programme on female foeticide among Primigravida mothers. The study was carried out at Shri Mahant Indresh Hospital, Patel Nagar Dehradun. Convenient sampling technique was used to collect data from 60 primigravida mothers admitted in obstetrics ward. Data was collected with a self-developed knowledge questionnaire which consisted of 17 multiple choice questions related to female foeticide and 15 questions based on attitude regarding female foeticide.
The result shows that after the structured teaching programme posttest shows that 16.6% had inadequate knowledge, 65% had moderate knowledge and 18.3% have adequate knowledge on female foeticide. While majority of primigravida mothers (92.6%) have agreed regarding positive aspects of female foeticide and almost 56.6% have disagreed regarding negative aspect of female foeticide.

Advances in Precision Medicine for Chronic Kidney Disease: Biomarkers, Risk Stratification, and Personalized Therapeutic Approaches

2026-08-10T10:09:44+00:00

Chronic kidney disease (CKD) is a biologically heterogeneous condition with great variations in the progression, complications and treatment responses in people with the same clinical syndromes. The concept of precision medicine is to address this variability by leveraging molecular, clinical and digital data to inform individual diagnosis and treatment. This review discusses the current understanding of precision nephrology, including the role of genetic susceptibility, metabolic dysfunction, inflammation, fibrosis, and multi-omics-defined disease subtypes. It also assesses newly introduced biomarkers such as KIM-1, NGAL, suPAR, DKK3, cystatin C, extracellular vesicles and multi-omics panels to detect, diagnose, and follow-up patients early. Dynamic prediction models, interpretable machine learning, artificial intelligence, and digital health platforms are discussed as tools for improving risk stratification and identifying patients at greatest risk of kidney function decline. Personalized therapeutic approaches include biomarker-guided treatment, RAAS inhibition, SGLT2 inhibitors, GLP-1 receptor agonists, pathway-specific therapies, gene and RNA therapeutics, and precision nutrition. Despite rapid progress, clinical implementation remains limited by inadequate validation, data fragmentation, cost, regulatory uncertainty, infrastructure demands, and inequitable access. Future progress will depend on standardized assays, interoperable data systems, representative cohorts, transparent algorithms, and prospective evidence of clinical utility. Finally, the integration of multi-omics, computational medicine, and personalized treatment could help facilitate mechanism-based classification and personalized treatment of CKD.

Early Detection of Chronic Kidney Disease Through Explainable Clinical Intelligence

2026-08-25T06:19:03+00:00

Chronic kidney disease (CKD) is a developing condition which frequently goes unrecognized in its early stages, resulting in delayed treatment and poor clinical outcomes. Early detection is, therefore, fundamental to slow down the disease sequence and increase patient care. An explainable clinical intelligence framework was designed for early detection of CKD by combining machine learning (ML) algorithms and explainable artificial intelligence (XAI). A publicly available dataset of 400 patient records with 24 routinely collected clinical variables was used for a retrospective analysis. Following data preprocessing, five supervised machine learning models Logistic Regression, Random Forest, XGBoost, LightGBM, and CatBoost were created and tested with an 80:20 stratified train-test split. The performance of the model was evaluated using accuracy, precision, recall, F1-score and receiver operating characteristic area under the curve (ROC-AUC). SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to justify the prediction results and discover the important clinical features. Ensemble learning models showed better predictive performance in which the highest classification accuracy was achieved by the models Random Forest, XGBoost, and CatBoost. Serum creatinine, haemoglobin, albumin, blood urea, and specific gravity were always found to be the most influential predictors of CKD in the explainability analysis. The results of these studies show that the fusion of effective machine learning models with explainable AI offers a reliable, transparent, and clinically significant approach to early detection of CKD.

Acute Kidney Injury as a Predictor of Remission and Relapse in Adult Minimal Change Disease: A Cohort Study Using Time-to-Event Analysis

2026-08-07T07:48:40+00:00

Acute kidney injury (AKI) is a common complication in adult minimal change disease (MCD), yet its influence on remission and relapse remains insufficiently characterized. This observational cohort study evaluated the association of AKI at presentation with time to complete remission and relapse in 174 adults with biopsy-proven MCD. Patients were classified according to AKI status at diagnosis. Time to complete remission and time to relapse after remission were assessed using Kaplan–Meier analysis and compared with the log-rank test, while Cox proportional hazards models were used to identify predictors of relapse. AKI was present in 40.8% of patients. All patients achieved complete remission during follow-up, with cumulative remission rates of 66.7% at 4 weeks and 87.4% at 8 weeks. Patients without AKI achieved remission more rapidly than those with AKI, with higher remission rates at 4 weeks (77.7% vs. 50.7%) and 8 weeks (92.2% vs. 80.3%), and a shorter median time to remission (24 vs. 28 days; log-rank p < 0.001). Relapse occurred in 64.9% of patients and was less frequent in the non-AKI group than in the AKI group (55.3% vs. 78.9%). Patients without AKI also experienced later relapse, with a longer median time to relapse (132 vs. 108 days; log-rank p < 0.001). In multivariable analysis, AKI remained independently associated with relapse (HR 1.65, 95% CI 1.14–2.39), whereas older age was protective and low serum albumin increased relapse risk. These findings indicate that AKI is a clinically relevant prognostic factor in adult MCD, associated with delayed remission and a higher and earlier risk of relapse.

Current and Emerging Nephroprotective Strategies in Chronic Kidney Disease: From RAAS Inhibition to Novel Therapeutics

2026-08-21T13:44:41+00:00

Multidimensional nephroprotection is necessary for chronic kidney disease (CKD) as haemodynamic, metabolic, inflammatory and fibrotic mechanisms play key roles in disease progression. The objective of this study was to assess the use of known and new nephroprotective drugs in adults who had CKD in a cross-sectional, population-based study design. Studies with persons 20 years of age or older were considered. CKD was defined as urine albumin creatinine ratio ≥ 30 mg/g and/or estimated glomerular filtration rate < 60 mL/min/1.73 m². Angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, mineralocorticoid receptor antagonists, and sodium-glucose cotransporter-2 inhibitors were the categories of prescription drugs. Multivariable logistic regression and survey-weighted descriptive analyses were carried out. Angiotensin receptor blockers (ARBs) or angiotensin-converting enzyme (ACE) inhibitors were used by 43.4% of the 1,003 persons with CKD. Mineralocorticoid receptor antagonists and sodium-glucose cotransporter-2 inhibitors were utilised by just 3.6% and 2.0% of individuals, respectively. The prevalence of use of RAAS inhibitors was 77.2% among persons with diabetes and hypertension. There was a positive association between increasing age, diabetes, and hypertension and increasing use of RAAS inhibitors, with hypertension having the greatest association. In summary, fewer that half of adults with the CKD received this therapy, and generally RAAS inhibition was the most widely used nephroprotective modality. The low adoption of emerging agents shows that there is still a chance to bridge the gap between therapeutic progress and actual deployment.

Early Risk Stratification of Chronic Kidney Disease and Dialysis Requirement Using Clinical Data

2026-08-14T07:51:17+00:00

Chronic Kidney Disease (CKD) is a long-term condition which is usually not diagnosed until there is significant renal damage, which can lead to dialysis and other serious complications. Thus, there is a need for early identification of high risk patients for better clinical outcome and disease burden reduction. This study introduces a machine learning-based approach for early risk-stratification of CKD and predicting the dialysis requirement from routine clinical data. The framework consists of two parts: demographic data, laboratory biomarkers and comorbidity data, followed by data preprocessing, exploratory analysis, development of predictive models and comparison of their performance. Several supervised machine learning models were developed and evaluated with the common performance metrics such as accuracy, precision, recall, F1-score and ROC-AUC. The most reliable predictive model was identified to differentiate the cases of CKD and to estimate dialysis requirement, when compared with the other models. The study results showed that machine learning approaches could be beneficial for early clinical decision making, as they can provide timely assessment of risk, better patient management and enable proactive intervention in patients at risk of CKD progression.

Novel Biomarkers for Early Detection and Prognosis of Diabetic Kidney Disease

2026-08-26T13:01:53+00:00

Diabetic kidney disease (DKD) is a common microvascular complication of diabetes mellitus and a major cause of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) in the world. While traditional markers, such as urinary albumin-to-creatinine ratio, serum creatinine, and estimated glomerular filtration rate remain pivotal in clinical evaluation, they often fall short in identifying subclinical renal damage and in predicting the trajectory of a particular disease. This study assesses newer markers of early detection, prognostic stratification and therapeutic monitoring of DKD. It covers various biomarkers of glomerular and tubular injury, inflammation, oxidative stress, fibrosis and metabolic dysregulation such as nephrin, podocin, kidney injury molecule-1, neutrophil gelatinase-associated lipocalin, tumour necrosis factor receptors, CCL20, trimethylamine N-oxide and fibrosis-related mediators. The study also discusses genomic, epigenomic, transcriptomic, proteomic, and metabolomic methods, highlighting the potential of combination of multi-omics, systems biology, artificial intelligence, and machine learning to enhance personalized risk prediction. The use of a panel of biomarkers including molecular, clinical, imaging, and digital more accurate for diagnosis and prognosis than a single-marker approach. But challenges of external validation, assay variability, high assay costs, regulatory demands and the ability to integrate advanced testing into routine workflows have limited clinical translation. Thus, large-scale prospective studies, common analytical procedures and cost-effectiveness analyses are needed. The use of validated molecular markers in conjunction with current tools leads to earlier intervention, tailored treatment choices, more effective therapeutic monitoring, and better long-term renal and cardiovascular outcomes for DKD.

Clinical Profile and Prognostic Outcomes of Adult-Onset Nephrotic Syndrome With Acute Kidney Injury: A Prospective Cohort Study at Bach Mai Hospital

2026-08-07T07:55:42+00:00

Nephrotic syndrome (NS) complicated by acute kidney injury (AKI) is associated with substantial morbidity and an increased risk of long-term renal dysfunction, yet prospective data on histopathological patterns, treatment response, and renal outcomes in this population remain limited. In this prospective observational cohort study, we enrolled 122 adults aged 18–60 years with newly diagnosed NS and concurrent AKI at the Kidney and Urology Center of Bach Mai Hospital, Hanoi, Vietnam, between 2023 and 2024, and followed them for 12 months. Clinical and biochemical parameters, renal biopsy findings, and treatment responses were evaluated, and AKI was classified according to AKIN criteria. Membranous glomerulonephritis (MGN, 36.9%) and focal segmental glomerulosclerosis (FSGS, 35.2%) were the most common histological subtypes, followed by minimal change disease (MCD, 23.0%). Pre-renal AKI accounted for 86.1% of cases, and AKIN Stage I was the most frequent stage at presentation (51.6%). Over 12 months, significant improvements were observed in serum creatinine, uric acid, lipid profile, proteinuria, and hematuria, with serum creatinine decreasing from 1.68 to 1.26 mg/dL and proteinuria from 5.16 to 1.7 g/24 h (p < 0.001 for all). Renal recovery was most frequent in patients with AKIN Stage I (95.2%) and least frequent in those with Stage III disease (30.0%). Treatment response also varied markedly by histological subtype: complete remission occurred in all patients with MCD, in 55.8% of those with FSGS, and in none of those with MGN. These findings indicate that histopathological subtype and AKIN stage are important predictors of renal recovery and therapeutic response in adults with NS-associated AKI, and support the clinical value of early recognition of pre-renal causes together with biopsy-guided management to improve outcomes.

Clinical Biomarker-Based Prediction of Chronic Kidney Disease Using Explainable Machine Learning

2026-08-22T14:49:29+00:00

Chronic kidney disease (CKD) is a progressive disease that needs to be diagnosed properly to slow the progression of the disease and its complications. The authors of this study suggest a clinical biomarker-based prediction framework that can be improved with explainable machine learning to enhance the accuracy and interpretability of the classification of CKD. Prior to the development of the models, the publicly available CKD dataset consisting of 400 patient records and 25 clinical attributes was preprocessed by imputing missing values, encoding categorical features, and normalizing the data. The performance of a range of supervised machine learning algorithms, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes and Extreme Gradient Boosting (XGBoost) was assessed using the standard performance measures. The best predictive model of the models evaluated was the Random Forest classifier. Explainable Artificial Intelligence (XAI) was used to incorporate the contribution of each biomarker and hemoglobin, serum creatinine, packed cell volume, specific gravity, and albumin were found to be the most significant biomarkers. The results show how a combination of explainable ML and accessible clinical biomarkers can offer a precise, transparent, and clinically interpretable framework for early CKD diagnosis, risk stratification, and informed clinical decision making.